Sentence examples for models to recognize from inspiring English sources

It is critically important for any developer of AWE models to recognize that models that are equally effective in predicting an external criterion may advantage/disadvantage different groups.

Professor Matthew Botvinick, for example, is one of a cadre of researchers using computer models to recognize patterns of brain activity in collections of whole brain images obtained by placing living, thinking human volunteers inside a specially configured MRI scanner.

In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input output patterns.

In our experiments, removing POS tags led to the largest negative impact on the results, indicating the importance of this feature for training models to recognize chemical entities.

It is a niche market that currently requires manual human input in order to train ML models to recognize images in a way that a human would, and Human in the Loop is part of a growing community of "impact sourcing" enterprises that is dedicated to providing employment to vulnerable groups in this sector.

Motivated by these studies and the potential of AV-ASR in human-computer interfaces [12], we propose to introduce a pose normalization step in a system designed for frontal views, i.e., we generate virtual frontal views from the non-frontal images and rely on the existing frontal visual models to recognize speech.

Second, it forces the model to recognize that the physics governing flame front propagation are only valid locally at the front.

All right, so next, we wanted to see if we could use this insight to build a hierarchical computational model to recognize actions.

Severe flood events in Kelantan, Malaysia cause damage to both life and property every year, and therefore the development of flood model to recognize the susceptible areas in watersheds is important for decision makers.

Introduction of information efficiency measures improves ability of the model to recognize the approaching reversal of long-term trend from temporary market "nervousness" and can be useful for calibrating stock trading strategy.

A pile of dog photographs cannot build a model to recognize their barks.